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--- |
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license: mit |
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base_model: naver-clova-ix/donut-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- bleu |
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- wer |
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model-index: |
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- name: donut_experiment_bayesian_trial_15 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# donut_experiment_bayesian_trial_15 |
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This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5777 |
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- Bleu: 0.0659 |
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- Precisions: [0.8158995815899581, 0.7434679334916865, 0.7060439560439561, 0.6644951140065146] |
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- Brevity Penalty: 0.0902 |
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- Length Ratio: 0.2936 |
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- Translation Length: 478 |
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- Reference Length: 1628 |
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- Cer: 0.7557 |
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- Wer: 0.8239 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2.349414650597281e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 2 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:| |
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| 0.0066 | 1.0 | 253 | 0.5790 | 0.0648 | [0.8305084745762712, 0.7686746987951807, 0.7262569832402235, 0.6843853820598007] | 0.0864 | 0.2899 | 472 | 1628 | 0.7593 | 0.8258 | |
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| 0.0143 | 2.0 | 506 | 0.5824 | 0.0663 | [0.8225469728601252, 0.7511848341232228, 0.7041095890410959, 0.6525974025974026] | 0.0908 | 0.2942 | 479 | 1628 | 0.7577 | 0.8265 | |
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| 0.009 | 3.0 | 759 | 0.5826 | 0.0640 | [0.8185654008438819, 0.7458033573141487, 0.7055555555555556, 0.6600660066006601] | 0.0876 | 0.2912 | 474 | 1628 | 0.7553 | 0.8248 | |
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| 0.0103 | 4.0 | 1012 | 0.5777 | 0.0659 | [0.8158995815899581, 0.7434679334916865, 0.7060439560439561, 0.6644951140065146] | 0.0902 | 0.2936 | 478 | 1628 | 0.7557 | 0.8239 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.1.0 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.1 |
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